Surface roughness model and parametric optimization in machining of GFRP composite: Taguchi and Response surface methodology approach

2015 ◽  
Vol 2 (4-5) ◽  
pp. 3065-3074 ◽  
Author(s):  
A.K. Parida ◽  
B.C. Routara ◽  
R.K. Bhuyan
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Parametric Optimization ◽  
Roughness Model ◽  
Gfrp Composite

Surface Roughness Prediction in the End Milling Parameters of Aluminum Nitride Ceramic by Response Surface Methodology

2010 ◽  
Vol 154-155 ◽  
pp. 626-633
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
Khaled A. Abou-El-Hossein
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
End Milling ◽  
Cubic Boron Nitride ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Roughness Model ◽  
Roughness Prediction ◽  
Grinding Tool

The present experimental study aimed to examine the selected machining parameters on Surface roughness in the machining of alumina nitride ceramic. The influence of cutting speed and feed rate were determined in end milling by using Cubic boron nitride grinding tool. The predictive surface roughness model has been developed by response surface methodology. The response surface contours with respect to input parameters are presented with the help of Design expert software. The adequacy of the model was tested by ANOVA.

Surface Roughness Model for Helical Milling of Die-Steel Based on Response Surface Methodology

2010 ◽  
Vol 431-432 ◽  
pp. 346-350 ◽  
Author(s):  
Xu Da Qin ◽  
Song Hua ◽  
Xiao Lai Ji ◽  
Shi Mao Chen ◽  
Wang Yang Ni
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Axial Direction ◽  
Helical Milling ◽  
Design Parameters ◽  
Die Steel ◽  
Roughness Model ◽  
Milling Operation ◽  
Hole Making

Holes making process is widely applied in die steel machining, Helical milling a hole, also called orbital drill, is hole making process by milling in which the center of end mill orbits around the center of the hole while spinning on its axis and moving in the axial direction. The paper presents the secondary regression prediction model of the holes surface roughness for helical milling of die-steel. To minimize the number of experiments for the design parameters, response surface methodology (RSM) with orthogonal rotatable central composite design is used. By means of variance analyses and additional cutting experiments, the adequacy of this model is confirmed. The model will be helpful in selecting cutting conditions to meet surface finish requirements in helical milling operation.

Surface Roughness Prediction Model in Machining of 34CrMo4 Steel with Several Tools Using Response Surface Methodology

2012 ◽  
Vol 445 ◽  
pp. 90-95
Author(s):  
Hamed Barghikar ◽  
Amin Poursafar ◽  
Abbas Amrollahi
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Mean Values ◽  
Roughness Model ◽  
34Crmo4 Steel

The surface roughness model in the turning of 34CrMo4 steel was developed in terms of cutting speed, feed rate and depth of cut and tool nose radius using response surface methodology. Machining tests were carried out using several tools with several tool radius under different cutting conditions. The roughness equations of cutting tools when machining the steels were achieved by using the experimental data. The results are presented in terms of mean values and confidence levels.The established equation and graphs show that the feed rate and cutting speed were found to be main influencing factor on the surface roughness. It increased with increasing the feed rate and depth of cut, but decreased with increasing the cutting speed, respectively. The variance analysis for the second-order model shows that the interaction terms and the square terms were statistically insignificant. However, it could be seen that the first-order affect of feed rate was significant while cutting speed and depth of cut was insignificant.The predicted surface roughness model of the samples was found to lie close to that of the experimentally observed ones with 95% confident intervals.

Parametric Optimization in Drilling Operation Using Response Surface Approach

2016 ◽  
Vol 1137 ◽  
pp. 117-131
Author(s):  
Kamaljit Singh Boparai ◽  
Sandeep Singh ◽  
Amritpal Singh
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Metal Cutting ◽  
Parametric Optimization ◽  
Machining Parameters ◽  
Surface Approach ◽  
Contour Plots ◽  
Cutting Processes

Modeling and optimization of machining parameters are the indispensable elements in modern metal cutting processes. The present study realize the interaction of drilling input process parameters such as spindle speed, feed rate and number of holes and their influence on the surface roughness, diameter and position of hole obtained in drilling of mild steel. The contour plots were generated to highlights the interaction of process parameters as well as their effect on responses. An empirical model of surface roughness, diameter and position of hole was developed using response surface methodology (RSM). The model fitted and measured values were quite close, which indicates that the developed models can be effectively used to predict the respective response. The process parameters are optimized using desirability-based approach response surface methodology.

scholarly journals Assessment of cutting force and surface roughness in LM6/SiC p using response surface methodology

2017 ◽  
Vol 15 (3) ◽  
pp. 283-296 ◽  
Author(s):  
Aezhisai Vallavi Muthusamy Subramanian ◽  
Mohan Das Gandhi Nachimuthu ◽  
Velmurugan Cinnasamy
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface

Parameter Optimization of Ball End Milling Process on Inconel 718 Using RSM and TLBO Algorithm

2015 ◽  
Vol 15 (3) ◽  
pp. 293-300 ◽  
Author(s):  
Nandkumar N. Bhopale ◽  
Nilesh Nikam ◽  
Raju S. Pawade
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Surface Integrity ◽  
Inconel 718 ◽  
Tool Path ◽  
End Milling ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Ball End Milling

AbstractThis paper presents the application of Response Surface Methodology (RSM) coupled with Teaching Learning Based Optimization Technique (TLBO) for optimizing surface integrity of thin cantilever type Inconel 718 workpiece in ball end milling. The machining and tool related parameters like spindle speed, milling feed, axial depth of cut and tool path orientation are optimized with considerations of multiple response like deflection, surface roughness, and micro hardness of plate. Mathematical relationship between process parameters and deflection, surface roughness and microhardness are found out by using response surface methodology. It is observed that after optimizing the process that at the spindle speed of 2,000 rpm, feed 0.05 mm/tooth/rev, plate thickness of 5.5 mm and 15° workpiece inclination with horizontal tool path gives favorable surface integrity.

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